Here’s a first look at Gemini Robotics 2 from @GoogleDeepMind on the FR3 Duo: 20 minutes of uninterrupted, real-time tool kitting. Notice the emergent recovery behaviours - generalized dexterity & precision at a whole new level. More demos dropping over the next days! Stay tuned!

To be truly useful, a robot needs the dexterity that humans take for granted. But asking AI models to coordinate these motions is incredibly complicated. In this demo, we pushed Apollo 2 to master fine motor skills and complete tricky kitchen chores ↓

Gemini Robotics 2 moves physical AI beyond tabletop tasks, enabling intelligent whole-body control for humanoids. Watch @Apptronik's Apollo 2 process a single prompt to reach, bend, and pick up a watering can ↓

Meet Lyria 3.5, the newest music model from @GoogleDeepMind, now powering Flow Music: 🎤 Vocals: More expressive, dynamic singing 🎸 Musicality: Richer arrangements with tracks that flow naturally. 🎛️ Controls: Better at following creative directions - now featuring BPM control and exportable stems for full-length songs! Try it now at 🧵

We’re expanding our work with the US Dept. of @ENERGY on the Genesis Mission – an initiative to double the pace of scientific discovery within a decade. 🧪 By committing $40M in AI tokens and @GoogleCloud credits, more lab researchers will gain access to Gemini and other AI models. →

Fascinating application of @GoogleDeepMind's AlphaFold in protein engineering! 🧬 As we approach 5 years since the AlphaFold Database was launched, it’s great to see it being used to fill in structural blind spots. While still early, it’s a brilliant example of how open access to AI-predicted structures is empowering experimental biologists to target complex health challenges.

We’re rolling out three new models to make AI agents faster, smarter, and cheaper at scale: 🔵 Gemini 3.6 Flash: It uses fewer tokens than 3.5 Flash to deliver higher quality work at the exact same cost. 🔵 Gemini 3.5 Flash-Lite: A fast, cost-effective option for everyday tasks like processing documents and agentic search. 🔵 Gemini 3.5 Flash Cyber: A cybersecurity model built to find and patch critical software vulnerabilities.

We're announcing a major update to Weather Lab, our interactive website for sharing Google’s AI weather models from @GoogleDeepMind and @GoogleResearch:

The biosecurity landscape is rapidly evolving. To stay ahead of future outbreaks, we’re partnering with @IsomorphicLabs to outline our approach to bioresilience. Here’s how we’re deploying frontier AI to build proactive defenses for global health →

From proposing hypotheses to designing experiments, AI agents are starting to reshape scientific discovery. But the hardest part is testing these ideas in the real world. Our essay explores the growing validation bottleneck and outlines four priorities for policymakers and funders. →

A model’s chain of thought acts like a scratch pad, offering a window into its reasoning. 📝 On the latest episode of our podcast, host @fryrsquared sits down with @NeelNanda5 to explore interpretability – the science of reverse engineering how neural networks learn and think. Timecodes: 00:00 Introduction 02:41 Motivation for interpretability research 04:01 Mechanistic interpretability 08:14 Chain of thought monitoring 18:14 Interpretability techniques 35:00 Auditing models for safety 48:53 What comes next for interpretability

AlphaEvolve is now GA on Google Cloud! Co-developed with @GoogleDeepMind, AlphaEvolve is a Gemini-powered evolutionary agent that autonomously designs and discovers highly optimized code to solve your hardest problems and algorithmic bottlenecks across industries:

As @Apptronik expands their Robot Park facility, our research partnership means real-world data collected by the latest Apollo 2 humanoid platform will help train and advance Gemini Robotics. 🤖 Find out more →

I am excited to share our latest work from the Discovery team at @GoogleDeepMind: COrigami, an end-to-end pipeline for co-designing origami with Gemini!  Origami is a unique mix of math, art and design. Creating origami involves turning abstract concepts into real-world objects, using the math of flat foldability. To tackle this, COrigami calls Gemini to generate a semantic stick figure—an abstract JSON code—refined through a visual feedback loop. It then calls custom packing, solving, shaping, and simulation tools. Driven by another self-improving reinforcement learning (RL) loop, the system produces visually recognisable models represented as SVG crease patterns. Despite scarce data availability, this approach demonstrates how combining RL with frontier models like Gemini can assist human creativity and produce physical art. The generated patterns serve as mathematically grounded starting points for origami artists to fold and shape into a final, physical design. The models below were produced by our system and then folded and shaped by @brandon_w0ng.   Read the full paper here:

What does it mean to be an ethicist and a philosopher working in an AI company? What questions do we explore – and can we affect positive change? For a deep exploration of these themes, check out a new long-read by Bobby Baird in the @guardian today...